
"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

a       :						Optional[Any]       		=		2




class 		a_    :







    def __init__(      self      :							Tuple  ,       *,  # begin keyword-only arguments
        __UpperCamelCase      :							str="<s>"  ,       __UpperCamelCase      :							str="<pad>"  ,       __UpperCamelCase      :							int="</s>"  ,       __UpperCamelCase      :							Tuple="<unk>"  ,       __UpperCamelCase      :							int=None  ,       )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = bos, unk, pad, eos
        _UpperCAmelCase        = []
        _UpperCAmelCase        = []
        _UpperCAmelCase        = {}
        _UpperCAmelCase        = self.add_symbol(__UpperCamelCase  )
        _UpperCAmelCase        = self.add_symbol(__UpperCamelCase  )
        _UpperCAmelCase        = self.add_symbol(__UpperCamelCase  )
        _UpperCAmelCase        = self.add_symbol(__UpperCamelCase  )
        if extra_special_symbols:
            for s in extra_special_symbols:
                self.add_symbol(__UpperCamelCase  )
        _UpperCAmelCase        = len(self.symbols  )







    def __eq__(      self      :							str  ,       __UpperCamelCase      :							int  )		->Tuple:






        '''simple docstring'''






        return self.indices == other.indices







    def __getitem__(      self      :							int  ,       __UpperCamelCase      :							List[Any]  )		->Union[str, Any]:






        '''simple docstring'''






        if idx < len(self.symbols  ):
            return self.symbols[idx]
        return self.unk_word







    def __len__(      self      :							List[Any]  )		->Optional[Any]:






        '''simple docstring'''






        return len(self.symbols  )







    def __contains__(      self      :							Tuple  ,       __UpperCamelCase      :							Union[str, Any]  )		->Any:






        '''simple docstring'''






        return sym in self.indices







    @classmethod
    def   _snake_case       (      cls      :							Tuple  ,       __UpperCamelCase      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = cls()
        d.add_from_file(__UpperCamelCase  )
        return d







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Optional[Any]=1  ,       __UpperCamelCase      :							List[Any]=False  )		->Dict:






        '''simple docstring'''






        if word in self.indices and not overwrite:
            _UpperCAmelCase        = self.indices[word]
            _UpperCAmelCase        = self.count[idx] + n
            return idx
        else:
            _UpperCAmelCase        = len(self.symbols  )
            _UpperCAmelCase        = idx
            self.symbols.append(__UpperCamelCase  )
            self.count.append(__UpperCamelCase  )
            return idx







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        return 0







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Optional[int]  )		->Dict:






        '''simple docstring'''






        if isinstance(__UpperCamelCase  ,       __UpperCamelCase  ):
            try:
                with open(__UpperCamelCase  ,       """r"""  ,       encoding="""utf-8"""  ) as fd:
                    self.add_from_file(__UpperCamelCase  )
            except FileNotFoundError as fnfe:
                raise fnfe
            except UnicodeError:
                raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(__UpperCamelCase  )  )
            return

        _UpperCAmelCase        = f.readlines()
        _UpperCAmelCase        = self._load_meta(__UpperCamelCase  )

        for line in lines[indices_start_line:]:
            try:
                _UpperCAmelCase     ,_UpperCAmelCase        = line.rstrip().rsplit(""" """  ,       1  )
                if field == "#fairseq:overwrite":
                    _UpperCAmelCase        = True
                    _UpperCAmelCase     ,_UpperCAmelCase        = line.rsplit(""" """  ,       1  )
                else:
                    _UpperCAmelCase        = False
                _UpperCAmelCase        = int(__UpperCamelCase  )
                _UpperCAmelCase        = line
                if word in self and not overwrite:
                    raise RuntimeError(
                        """Duplicate word found when loading Dictionary: '{}'. """
                        """Duplicate words can overwrite earlier ones by adding the """
                        """#fairseq:overwrite flag at the end of the corresponding row """
                        """in the dictionary file. If using the Camembert model, please """
                        """download an updated copy of the model file.""".format(__UpperCamelCase  )  )
                self.add_symbol(__UpperCamelCase  ,       n=__UpperCamelCase  ,       overwrite=__UpperCamelCase  )
            except ValueError:
                raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""  )







def 			_UpperCamelCase						(    _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = dict((re.sub(R"""@@$"""       ,   """"""       ,   _A   ), v) if k.endswith("""@@"""   ) else (re.sub(R"""$"""       ,   """</w>"""       ,   _A   ), v) for k, v in d.items()   )
    _UpperCAmelCase        = """<s> <pad> </s> <unk>""".split()
    # restore the special tokens
    for k in keep_keys:
        del da[F"""{k}</w>"""]
        _UpperCAmelCase        = d[k]  # restore
    return da


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    if not os.path.exists(_A   ):
        raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!"""   )
    os.makedirs(_A       ,   exist_ok=_A   )
    print(F"""Writing results to {pytorch_dump_folder_path}"""   )

    # handle various types of models

    _UpperCAmelCase        = os.path.join(_A       ,   """checkpoint.pt"""   )
    if not os.path.isfile(_A   ):
        raise ValueError(F"""path to the file {checkpoint_file} does not exist!"""   )
    _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )

    _UpperCAmelCase        = chkpt["""cfg"""]["""model"""]

    # dicts
    _UpperCAmelCase        = os.path.join(_A       ,   """dict.txt"""   )
    if not os.path.isfile(_A   ):
        raise ValueError(F"""path to the file {dict_file} does not exist!"""   )
    _UpperCAmelCase        = Dictionary.load(_A   )
    _UpperCAmelCase        = rewrite_dict_keys(src_dict.indices   )
    _UpperCAmelCase        = len(_A   )
    _UpperCAmelCase        = os.path.join(_A       ,   VOCAB_FILES_NAMES["""vocab_file"""]   )
    print(F"""Generating {src_vocab_file} of {src_vocab_size} records"""   )
    with open(_A       ,   """w"""       ,   encoding="""utf-8"""   ) as f:
        f.write(json.dumps(_A       ,   ensure_ascii=_A       ,   indent=_A   )   )

    # merges_file (bpecodes)
    _UpperCAmelCase        = os.path.join(_A       ,   """bpecodes"""   )
    if not os.path.isfile(_A   ):
        raise ValueError(F"""path to the file {bpecodes_file} does not exist!"""   )

    _UpperCAmelCase        = os.path.join(_A       ,   VOCAB_FILES_NAMES["""merges_file"""]   )
    shutil.copyfile(_A       ,   _A   )

    # model config
    _UpperCAmelCase        = os.path.join(_A       ,   """config.json"""   )

    _UpperCAmelCase        = {
        """activation_dropout""": args["""activation_dropout"""],
        """architectures""": ["""BioGptForCausalLM"""],
        """attention_probs_dropout_prob""": args["""attention_dropout"""],
        """bos_token_id""": 0,
        """eos_token_id""": 2,
        """hidden_act""": args["""activation_fn"""],
        """hidden_dropout_prob""": args["""dropout"""],
        """hidden_size""": args["""decoder_embed_dim"""],
        """initializer_range""": 0.02,
        """intermediate_size""": args["""decoder_ffn_embed_dim"""],
        """layer_norm_eps""": 1e-12,
        """layerdrop""": args["""decoder_layerdrop"""],
        """max_position_embeddings""": args["""max_target_positions"""],
        """model_type""": """biogpt""",
        """num_attention_heads""": args["""decoder_attention_heads"""],
        """num_hidden_layers""": args["""decoder_layers"""],
        """pad_token_id""": 1,
        """scale_embedding""": not args["""no_scale_embedding"""],
        """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
        """vocab_size""": src_vocab_size,
    }

    # good hparam defaults to start with

    print(F"""Generating {biogpt_model_config_file}"""   )
    with open(_A       ,   """w"""       ,   encoding="""utf-8"""   ) as f:
        f.write(json.dumps(_A       ,   ensure_ascii=_A       ,   indent=_A   )   )

    # tokenizer config
    _UpperCAmelCase        = os.path.join(_A       ,   _A   )

    _UpperCAmelCase        = {
        """bos_token""": """<s>""",
        """eos_token""": """</s>""",
        """model_max_length""": 1_0_2_4,
        """pad_token""": """<pad>""",
        """special_tokens_map_file""": None,
        """tokenizer_class""": """BioGptTokenizer""",
        """unk_token""": """<unk>""",
    }

    print(F"""Generating {biogpt_tokenizer_config_file}"""   )
    with open(_A       ,   """w"""       ,   encoding="""utf-8"""   ) as f:
        f.write(json.dumps(_A       ,   ensure_ascii=_A       ,   indent=_A   )   )

    # model
    _UpperCAmelCase        = chkpt["""model"""]

    # remove unneeded keys
    _UpperCAmelCase        = [
        """decoder.version""",
    ]
    for k in ignore_keys:
        model_state_dict.pop(_A       ,   _A   )

    _UpperCAmelCase        = list(model_state_dict.keys()   )
    for layer_name in layer_names:
        if layer_name.endswith("""output_projection.weight"""   ):
            _UpperCAmelCase        = model_state_dict.pop(_A   )
        else:
            _UpperCAmelCase        = model_state_dict.pop(_A   )

    _UpperCAmelCase        = BioGptConfig.from_pretrained(_A   )
    _UpperCAmelCase        = BioGptForCausalLM(_A   )

    # check that it loads ok
    model_new.load_state_dict(_A   )

    # save
    _UpperCAmelCase        = os.path.join(_A       ,   _A   )
    print(F"""Generating {pytorch_weights_dump_path}"""   )
    torch.save(_A       ,   _A   )

    print("""Conversion is done!"""   )


if __name__ == "__main__":
 a       :						Union[str, Any]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--biogpt_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help=(
         '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
         ''' bpecodes, etc.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Dict       		=		parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a       :						List[str]       		=		logging.get_logger(__name__)




class 		a_    :







    def __init__(      self      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = question_encoder
        _UpperCAmelCase        = generator
        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  )		->Dict:






        '''simple docstring'''






        if os.path.isfile(__UpperCamelCase  ):
            raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""  )
        os.makedirs(__UpperCamelCase  ,       exist_ok=__UpperCamelCase  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """question_encoder_tokenizer"""  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """generator_tokenizer"""  )
        self.question_encoder.save_pretrained(__UpperCamelCase  )
        self.generator.save_pretrained(__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Optional[Any]  ,       __UpperCamelCase      :							Tuple  ,       **__UpperCamelCase      :							Optional[Any]  )		->Tuple:






        '''simple docstring'''






        from ..auto.tokenization_auto import AutoTokenizer

        _UpperCAmelCase        = kwargs.pop("""config"""  ,       __UpperCamelCase  )

        if config is None:
            _UpperCAmelCase        = RagConfig.from_pretrained(__UpperCamelCase  )

        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.question_encoder  ,       subfolder="""question_encoder_tokenizer"""  )
        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.generator  ,       subfolder="""generator_tokenizer"""  )
        return cls(question_encoder=__UpperCamelCase  ,       generator=__UpperCamelCase  )







    def __call__(      self      :							Any  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Optional[Any]  )		->Optional[Any]:






        '''simple docstring'''






        return self.current_tokenizer(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Tuple  )		->Any:






        '''simple docstring'''






        return self.generator.batch_decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[int]  ,       *__UpperCamelCase      :							Optional[int]  ,       **__UpperCamelCase      :							List[str]  )		->Union[str, Any]:






        '''simple docstring'''






        return self.generator.decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.generator







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[List[str]] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							str = "longest"  ,       __UpperCamelCase      :							str = None  ,       __UpperCamelCase      :							bool = True  ,       **__UpperCamelCase      :							Tuple  ,       )		->BatchEncoding:






        '''simple docstring'''






        warnings.warn(
            """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
            """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
            """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
            """details"""  ,       __UpperCamelCase  ,       )
        if max_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            __UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            text_target=__UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        _UpperCAmelCase        = labels["""input_ids"""]
        return model_inputs